# coding=utf-8

# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

import moxing as mox
import argparse
import os
import glob
import random
import shutil
import time
import warnings
from inceptionresnetv2 import InceptionResNetV2
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import torch.npu
import math
from apex import amp
from apex.optimizers import NpuFusedSGD

CALCULATE_DEVICE = "npu:0"
# ---------modelarts modification-----------------
# 缓存数据集目录
CACHE_DATA_URL = '/cache/data_url/'
# 缓存输出目录
CACHE_TRAIN_URL = '/cache/training/'
# ---------modelarts modification end-------------

model_names = sorted(name for name in models.__dict__
                     if name.islower() and not name.startswith("__")
                     and callable(models.__dict__[name]))

cur_path = os.path.abspath(os.path.dirname(__file__))
with open(os.path.join(cur_path, '../url.ini'), 'r') as f:
    content = f.read()
    inceptionresnetv2 = content.split('inceptionresnetv2=')[1].split('\n')[0]

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
# ------------modelarts modification-----------------
# 数据集目录
parser.add_argument('--data_url', type=str,
                    default='/cache/data_url/', help='the training data')
# 模型输出目录
parser.add_argument('--train_url', default="/cache/training/", type=str,
                    help='the path model saved')
# 是否转为onnx
parser.add_argument('--onnx', default=True, action='store_true',
                    help="convert pth model to onnx")
# -----------modelarts modification end-------------

parser.add_argument('--data', metavar='DIR',
                    help='path to dataset')
parser.add_argument('--exp_subpath', type=str, default='InceptionResNetV2')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=90, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=128, type=int,
                    metavar='N',
                    help='mini-batch size (default: 256), this is the total '
                         'batch size of all GPUs on the current node when '
                         'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=0.045, type=float,
                    metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
                    help='momentum')
parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
                    metavar='W', help='weight decay (default: 1e-4)',
                    dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=10, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
                    help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                    help='use pre-trained model')
parser.add_argument('--world-size', default=-1, type=int,
                    help='number of nodes for distributed training')
parser.add_argument('--rank', default=-1, type=int,
                    help='node rank for distributed training')
parser.add_argument('--dist-url', default=inceptionresnetv2, type=str,
                    help='url used to set up pretained training')
parser.add_argument('--dist-backend', default='nccl', type=str,
                    help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int, help='GPU id to use.')
parser.add_argument('--npu', default=None, type=int,
                    help='NPU id to use.')
parser.add_argument('--multiprocessing-distributed', action='store_true',
                    help='Use multi-processing distributed training to launch '
                         'N processes per node, which has N GPUs. This is the '
                         'fastest way to use PyTorch for either single node or '
                         'multi node data parallel training')
parser.add_argument('--warmup', default=0, type=int, metavar='E',
                    help='number of warmup epochs')
parser.add_argument('--label-smoothing', default=0.0, type=float, metavar='S',
                    help='label smoothing')
parser.add_argument('--optimizer-batch-size', default=-1, type=int, metavar='N',
                    help='size of a total batch size, for simulating bigger batches using gradient accumulation')
parser.add_argument('--static-loss-scale', type=float, default=1,
                    help='Static loss scale, positive power of 2 values can improve fp16 convergence.')
parser.add_argument('-t', '--fine-tuning', action='store_true',
                    help='transfer learning + fine tuning - train only the last FC layer.')
parser.add_argument('--amp', default=False, action='store_true',
                    help='use amp to train the model')

best_acc1 = 0


def main():
    args = parser.parse_args()
    if args.npu is None:
        args.npu = 0
    global CALCULATE_DEVICE
    CALCULATE_DEVICE = "npu:{}".format(args.npu)
    torch.npu.set_device(CALCULATE_DEVICE)
    print("use ", CALCULATE_DEVICE)
    args = parser.parse_args()

    if args.seed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)
        cudnn.deterministic = True
        warnings.warn('You have chosen to seed training. '
                      'This will turn on the CUDNN deterministic setting, '
                      'which can slow down your training considerably! '
                      'You may see unexpected behavior when restarting '
                      'from checkpoints.')

    if args.gpu is not None:
        warnings.warn('You have chosen a specific GPU. This will completely '
                      'disable data parallelism.')

    if args.dist_url == "env://" and args.world_size == -1:
        args.world_size = int(os.environ["WORLD_SIZE"])

    args.distributed = args.world_size > 1 or args.multiprocessing_distributed

    ngpus_per_node = torch.cuda.device_count()
    if args.multiprocessing_distributed:
        # Since we have ngpus_per_node processes per node, the total world_size
        # needs to be adjusted accordingly
        args.world_size = ngpus_per_node * args.world_size
        # Use torch.multiprocessing.spawn to launch distributed processes: the
        # main_worker process function
        mp.spawn(main_worker, nprocs=ngpus_per_node,
                 args=(ngpus_per_node, args))
    else:
        # Simply call main_worker function
        main_worker(args.gpu, ngpus_per_node, args)


def main_worker(gpu, ngpus_per_node, args):
    global best_acc1
    args.gpu = gpu

    if args.gpu is not None:
        print("Use GPU: {} for training".format(args.gpu))

    if args.distributed:
        if args.dist_url == "env://" and args.rank == -1:
            args.rank = int(os.environ["RANK"])
        if args.multiprocessing_distributed:
            # For multiprocessing distributed training, rank needs to be the
            # global rank among all the processes
            args.rank = args.rank * ngpus_per_node + gpu
        dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                world_size=args.world_size, rank=args.rank)
    # create model
    if args.pretrained:
        print("=> using pre-trained model '{}'".format(args.exp_subpath))
        model = InceptionResNetV2()
        # model.load_state_dict(model_zoo.load_url(settings['url']))
        print("load the model by yourself")
        pretrained_dict = torch.load(
            "./model_best_acc12.6719_epoch2.pth.tar", map_location="cpu")
        model.load_state_dict(pretrained_dict, strict=False)
    else:
        print("=> creating model '{}'".format(args.exp_subpath))
        model = InceptionResNetV2()

    if args.fine_tuning:
        print("=> transfer learning + fine tuning(train only the last FC layer)")
        if args.exp_subpath == "InceptionResNetV2":
            model.parameters()
        else:
            print("Error: Fine-tuning is not supported on this architecture")
            exit(-1)
    else:
        model.parameters()

    for layer in model.modules():
        if isinstance(layer, nn.Linear):
            torch.nn.init.kaiming_normal_(layer.weight, a=math.sqrt(5), )

    if not torch.cuda.is_available():
        print('using CPU, this will be slow')
    elif args.distributed:
        # For multiprocessing distributed, DistributedDataParallel constructor
        # should always set the single device scope, otherwise,
        # DistributedDataParallel will use all available devices.
        if args.gpu is not None:
            torch.cuda.set_device(args.gpu)
            model.cuda(args.gpu)
            # When using a single GPU per process and per
            # DistributedDataParallel, we need to divide the batch size
            # ourselves based on the total number of GPUs we have
            args.batch_size = int(args.batch_size / ngpus_per_node)
            args.workers = int(
                (args.workers + ngpus_per_node - 1) / ngpus_per_node)
            model = torch.nn.parallel.DistributedDataParallel(
                model, device_ids=[args.gpu])
        else:
            model.cuda()
            # DistributedDataParallel will divide and allocate batch_size to all
            # available GPUs if device_ids are not set
            model = torch.nn.parallel.DistributedDataParallel(model)
    elif args.gpu is not None:
        torch.cuda.set_device(args.gpu)
        model = model.cuda(args.gpu)
    else:
        # DataParallel will divide and allocate batch_size to all available GPUs
        if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
            model.features = torch.nn.DataParallel(model.features)
            model.cuda()
        else:
            model = model.to(CALCULATE_DEVICE)
    model = model.to(CALCULATE_DEVICE)
    lr_policy = lr_cosine_policy(args.lr,
                                 args.warmup,
                                 args.epochs)
    # define loss function (criterion) and optimizer
    #criterion = nn.CrossEntropyLoss().cuda(args.gpu)
    criterion = nn.CrossEntropyLoss().to(CALCULATE_DEVICE)

    optimizer = NpuFusedSGD(
        model.parameters(),
        lr=args.lr,
        momentum=args.momentum,
        weight_decay=args.weight_decay,
    )
    model, optimizer = amp.initialize(
        model, optimizer, opt_level="O2", loss_scale=1024, verbosity=1)
    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            if args.npu is not None:
                checkpoint = torch.load(args.resume)
            elif args.gpu is None:
                checkpoint = torch.load(args.resume)
            else:
                # Map model to be loaded to specified single gpu.
                loc = 'cuda:{}'.format(args.gpu)
                checkpoint = torch.load(args.resume, map_location=loc)
            args.start_epoch = checkpoint['epoch']
            best_acc1 = checkpoint['best_acc1']
            if args.npu is not None:
                best_acc1 = best_acc1.to("npu:{}".format(args.npu))
            elif args.gpu is not None:
                # best_acc1 may be from a checkpoint from a different GPU
                best_acc1 = best_acc1.to(args.gpu)
            model.load_state_dict(checkpoint['state_dict'])
            # optimizer.load_state_dict(checkpoint['optimizer'])
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    cudnn.benchmark = True

    # Data loading code
    # ------------modelarts modification-----------------
    if not os.path.exists(CACHE_DATA_URL):
        os.makedirs(CACHE_DATA_URL)
    mox.file.copy_parallel(args.data_url, CACHE_DATA_URL)
    print("training data finish copy to %s." % CACHE_DATA_URL)
    # --------------modelarts modification end--------------

    traindir = os.path.join(CACHE_DATA_URL, 'train/')
    valdir = os.path.join(CACHE_DATA_URL, 'val/')

    normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5],
                                     std=[0.5, 0.5, 0.5])

    train_dataset = datasets.ImageFolder(
        traindir,
        transforms.Compose([
            transforms.RandomResizedCrop(299),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]))

    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(
            train_dataset)
    else:
        train_sampler = None

    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=(
            train_sampler is None),
        num_workers=args.workers, pin_memory=True, sampler=train_sampler)

    val_loader = torch.utils.data.DataLoader(
        datasets.ImageFolder(valdir, transforms.Compose([
            transforms.Resize(342),
            transforms.CenterCrop(299),
            transforms.ToTensor(),
            normalize,
        ])),
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True)

    if args.evaluate:
        validate(val_loader, model, criterion, args)
        return

    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            train_sampler.set_epoch(epoch)
        adjust_learning_rate(optimizer, epoch, args)

        # train for one epoch
        train(train_loader, model, criterion, optimizer, epoch, args)

        # evaluate on validation set
        acc1 = validate(val_loader, model, criterion, args)

        # remember best acc@1 and save checkpoint
        is_best = acc1 > best_acc1
        best_acc1 = max(acc1, best_acc1)
        file_name = "checkpoint_npu{}.pth".format(args.npu)
        modeltmp = model.cpu()
        if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                                                    and args.rank % ngpus_per_node == 0):
            save_checkpoint({
                'epoch': epoch + 1,
                'state_dict': model.state_dict(),
                'best_acc1': best_acc1,
                'optimizer': optimizer.state_dict(),
            }, is_best, file_name)
        modeltmp.to(CALCULATE_DEVICE)

    if args.onnx:
        convert(CACHE_TRAIN_URL, file_name)
    # --------------modelarts modification--------------------
    mox.file.copy_parallel(CACHE_TRAIN_URL, args.train_url)
    # --------------modelarts modification end----------------


def convert(model_path, filename):
    print('filename is ', filename)
    checkpoint = torch.load(os.path.join(
        model_path, filename), map_location='cpu')
    model = InceptionResNetV2()
    model.load_state_dict(checkpoint, False)
    model.eval()
    print(model)

    input_names = ["actual_input_1"]
    output_names = ["output1"]
    dummy_input = torch.randn(1, 3, 299, 299)
    torch.onnx.export(model, dummy_input, os.path.join(model_path, "InceptionResNetV2_npu.onnx"),
                      input_names=input_names, output_names=output_names, opset_version=11)


def train(train_loader, model, criterion, optimizer, epoch, args):
    if args.optimizer_batch_size < 0:
        batch_size_multiplier = 1
    else:
        tbs = 1 * args.batch_size
        if args.optimizer_batch_size % tbs != 0:
            print(
                "Warning: simulated batch size {} is not divisible by actual batch size {}"
                .format(args.optimizer_batch_size, tbs))
        batch_size_multiplier = int(args.optimizer_batch_size / tbs)
        print("BSM: {}".format(batch_size_multiplier))

    batch_time = AverageMeter('Time', ':6.3f')
    data_time = AverageMeter('Data', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(train_loader),
        [batch_time, data_time, losses, top1, top5],
        prefix="Epoch: [{}]".format(epoch))

    # switch to train mode
    model.train()
    optimizer.zero_grad()
    end = time.time()
    for i, (images, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        if args.gpu is not None:
            images = images.cuda(args.gpu, non_blocking=True)
        images = images.to(CALCULATE_DEVICE, non_blocking=True)

        target = target.to(torch.int32).to(CALCULATE_DEVICE, non_blocking=True)
        # if torch.cuda.is_available():
        #target = target.cuda(args.gpu, non_blocking=True)
        # compute output
        output = model(images)
        loss = criterion(output, target)

        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), images.size(0))
        top1.update(acc1[0], images.size(0))
        top5.update(acc5[0], images.size(0))

        # compute gradient and do SGD step
        if args.amp:
            with amp.scale_loss(loss, optimizer) as scaled_loss:
                scaled_loss.backward()
        else:
            loss.backward()
        optimizer.step()
        optimizer.zero_grad()

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                  'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                      epoch, i, len(train_loader), batch_time=batch_time,
                      data_time=data_time, loss=losses, top1=top1, top5=top5))
            print("batch_size:", args.batch_size,
                  '* FPS@all {:.3f}'.format(args.batch_size/batch_time.avg))


def validate(val_loader, model, criterion, args):
    batch_time = AverageMeter('Time', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    progress = ProgressMeter(
        len(val_loader),
        [batch_time, losses, top1, top5],
        prefix='Test: ')

    # switch to evaluate mode
    model.eval()

    with torch.no_grad():
        end = time.time()
        for i, (images, target) in enumerate(val_loader):
            if args.gpu is not None:
                images = images.cuda(args.gpu, non_blocking=True)
            images = images.to(CALCULATE_DEVICE, non_blocking=True)
            target = target.to(torch.int32).to(
                CALCULATE_DEVICE, non_blocking=True)
            # if torch.cuda.is_available():
            #target = target.cuda(args.gpu, non_blocking=True)
            images = images.to(CALCULATE_DEVICE, non_blocking=True)
            target = target.to(torch.int32).to(
                CALCULATE_DEVICE, non_blocking=True)
            # compute output
            output = model(images)
            loss = criterion(output, target)

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            losses.update(loss.item(), images.size(0))
            top1.update(acc1[0], images.size(0))
            top5.update(acc5[0], images.size(0))

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % args.print_freq == 0:
                print('Test: [{0}/{1}]\t'
                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                      'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                      'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                          i, len(val_loader), batch_time=batch_time, loss=losses,
                          top1=top1, top5=top5))
                print("batch_size:", args.batch_size,
                      '* FPS@all {:.3f}'.format(args.batch_size/batch_time.avg))

        # TODO: this should also be done with the ProgressMeter
        print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
              .format(top1=top1, top5=top5))

    return top1.avg


def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    if not os.path.exists(CACHE_TRAIN_URL):
        os.makedirs(CACHE_TRAIN_URL, 0o755)
    # ---------modelarts modification-----------------
    filepath = os.path.join(CACHE_TRAIN_URL, filename)
    torch.save(state, filepath)
    # ---------modelarts end-----------------


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        self.reset()
        self.start_count_index = 10

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)


class ProgressMeter(object):
    def __init__(self, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print('\t'.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'


def adjust_learning_rate(optimizer, epoch, args):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = args.lr * (0.1 ** (epoch // 30))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


def lr_policy(lr_fn, logger=None):
    if logger is not None:
        logger.register_metric('lr',
                               log.LR_METER(),
                               verbosity=dllogger.Verbosity.VERBOSE)

    def _alr(optimizer, iteration, epoch):
        lr = lr_fn(iteration, epoch)

        if logger is not None:
            logger.log_metric('lr', lr)
        for param_group in optimizer.param_groups:
            param_group['lr'] = lr

    return _alr


def lr_cosine_policy(base_lr, warmup_length, epochs, logger=None):
    def _lr_fn(iteration, epoch):
        if epoch < warmup_length:
            lr = base_lr * (epoch + 1) / warmup_length
        else:
            e = epoch - warmup_length
            es = epochs - warmup_length
            lr = 0.5 * (1 + np.cos(np.pi * e / es)) * base_lr
        return lr

    return lr_policy(_lr_fn, logger=logger)


if __name__ == '__main__':
    main()
